Beside the semantic type agreement, the implicit resemblance between an anaphor and its antecedents is another evidence useful for verifying the semantic association.

features 5, 6, and 7 are related to semantic association.


The method of calculating the similarity between two clauses and how to recognize contrast/list relation are described in detail below.

written texts are automatically converted into spoken texts based on paraphrasing technique [5,6] and then are inputted into speech synthesis.


Preposition SRL combined with [9] (P = precision, R = recall, F = F-score; above-baseline results in boldface) in the testing data, even when oracled outputs from all three subsystems are used (recall = 18.15%).

this further suggests that the semantic roles were tagged for only a small number of verbs in relatively fixed situations.


With graph refinement frameworks such as the one presented here, many of these resources may be improved automatically.

vERBOCEAN is a graph of semantic relations between verbs, with 3,477 verbs (nodes) and 22,306 relations (edges).


In this subsection we derive an estimate of the validity of inferring r 1,n given the set In the case of zero paths, we use simply P(r 1,n )=P(r), the probability of observing r between a pair of nodes from a sample set with no additional evidence.


Lexical entries (in fact not limited to verbs, in the case of FrameNet) falling under the same frame will share the same set of roles.

given the imperfection of existing automatic parsers, which are far from producing gold standard parses, many thus resort to shallow syntactic information from simple chunking, though results often turn out to be less satisfactory than with full parses.


We especially thank the three anonymous reviewers for their valuable suggestions and comments.

as time information increases, the error rate of NSHmm decreases drastically in close test as it dose in pos tagging task.


But NSHmm and VNSHmm can still model and make good use of those positional characteristics, and notable improvements have been achieved.

this paper suggests k=3 in NSHmm for pos tagging task.


About 40% of titles are quoted in "「」" or "『』" in our test corpus.

the definition of Recurrence feature is number of occurrences of the possible title in the input document.


Context deals with features surroundings titles.

it is even more difficult to decide which fragment of sentences might be a title than to determine if some fragment is a title.


We perform the following pre-processing steps.

we observe this improvement on both French and Czech.


We hypothesize that a large component of the error rate in the automatically induced text analysis tools generated by [22] is due to morphosyntactic differences between the source and target languages that are specific to each source-target language pair.

for each single-source tagger, we train on 31,000 lines of the parallel Bible between the source and target languages and test on 100 held-out lines of the Bible in the target language.


Such as, mthong "see", go "hear", ha go "understand", dran "remember", brjed "forget", ngo shes "recognize" and so on.

the inner structural relationship of compound verbs may produce influence on the objects and their types.


Even though the human judges underwent only minimal training, their poor-to-fair kappa scores indicate that this is a very hard problem.

there are a big variety of heterogeneous features that contribute to the temporal reference semantics of Chinese verbs.


To evaluate the performance of classifiers, we measure the standard classification accuracy where accuracy is defined as in equation 2 To measure how well the classifier does on each class respectively, we compute precision, recall, and F-measure, which are defined respectively in equation 3, (4) and 5  The evaluation is carried on the collapsed tense taxonomy that consists of three tense classes: present, past and future.

while there is certainly room for improvement, the tagging performance of our algorithm is quite promising.


Selection of the voice: Choose the voice of the target sentence among active, passive, causative, etc.

upper bound: If the dictionaries cover all light-verbs and deverbal nouns, the collection covers 24.1% (492,737 / 2,044,387) of tokens.


Likewise, paraphrases involving an LVC as in (3) and (4) (from [4]) have considerable similarities.

to generate this type of paraphrase, we need a computational model that is capable of the following operations: Change of the dependence: Change the dependences of the elements (a) and (b) due to the elimination of the original modifiee, the light-verb.


[10] reimplemented the method of [9] using a web, which may be a very large corpus, in order to collect example sentences.

considering the difference, we implemented the method of [8] to include the ambiguous relatives into relatives, but the method of [9] to exclude the ambiguous relatives.


We tried to compare our proposed method with the previous relative based methods.

instead, we build a co-occurrence frequency matrix (CFM) from a raw corpus that contains frequencies of words and word pairs.


The experimental results here suggest that our system has not been penalized very much when rich linguistic features are only available in its training phase.

the difference is that Z is a Named Entity when call is in Sense 1, and Z is usually a common NP or an adjective phrase (ADJP) when call is in Sense 3.


In the first experiment (where the weight of the modifier and head noun was the same), we observed that some of the test NCs matched with several training NCs with high similarity.

in the remainder of the paper, we detail the motivation for our work (Section 2), introduce the WordNet::Similarity system which we use to calculate word similarity (Section 3), outline the set of semantic relations used (Section 4), detail how we collected the data (Section 5), introduce the proposed method (Section 6), and describe experimental results (Section 7).


This indeed reflects the in-domain variability of text: Nikkei, Yomiuri and Encarta are highly edited text, following style guidelines; they also tend to have repetitious content.

we hope to run additional experiments to confirm these findings.


In MAP estimation methods, adaptation data is used to adjust the parameters of the background model so as to maximize the likelihood of the adaptation data [1].

the more similar the adaptation domain is to the background domain, the better the CER results are.


MCB is Majority Class Baseline, which marks every word as APPROPRIATE.

although it is not real spoken language, it works as a good substitute, as some researchers pointed out [2,11].


Lexical choice has been one of the central issues in NLG.

the kernel function of SVM was Gaussian RBF.


If F-ratio is more than 0.2, or if F-ratio is more than 0.1 and P-ratio is more than 0.2, the page is classified as spoken language page.

if the probability is estimated using the whole corpora, the features shift to the black triangle and diamond, and Baseline wrongly classified the two as inappropriate.


Its adaptation-guided retrieval makes it ultimately similar to our system.

denote C (A) as the case, where A={A 1 ,A 2 ,…A m } represents the feature set of the case.


the word number ratio of translation pair is on a basis of 1:1.


The reasons for this strategy are as follows: 1) terminology seldom consists of rare words out of GB2312, 2) the index space is dramatically reduced using GB2312 rather than the Unicode encoding so as to quicken the estimation speed.

the issue and the proposed method in this paper are distinctly different with Nagata's.


So we need to find a combined technique for reducing this "noise".

hybrid method using linguistic filters proved to be a suitable method for selecting terminological collocations, it has considerably improved the precision of the extraction which is much higher than that of purely statistical method.


For example, if the loanword "станц (station)" is to be concatenated with a genitive case suffix, "ын" should be selected from the five genitive case suffixes (i.e., ын, ийн, ы, ий, and н) based on the Mongolian grammar.

xu and Croft (1998) and Melucci and Orio (2003) independently proposed a languageindependent method for stemming, which analyzes a corpus in a target language and identifies an equivalent class consisting of an original form, inflected forms, and derivations.


This error occurs because the ending "-ологи (-ology)" does not appear in conventional Mongolian words.

to enhance the objectivity of the evaluation, we used only the phrases for which the two assessors agreed with respect to the part of speech and lemmatization.


We take the logarithm of FSR 3 Although there have been many existing works in this direction (Lua and Gan, 1994;Chien, 1997;Sun et al., 1998;Zhang et al., 2000;SUN et al., 2004), we have to skip the details of comparing MI due to the length limitation of this paper.

the performance of these measures is presented in table 2.


We can write the following SPE-style rule to account for its variation.

we also use parameter b in Step 4 as a way to discourage excessive splitting by tagging more morphemes as noise.


The LP algorithm has also been successfully applied in other NLP applications, such as word sense disambiguation (Niu et al 2005), text classification (Szummer and Jaakkola 2001;Blum and Chawla 2001;Belkin and Niyogi 2002;Zhu and Ghahramani 2002;Zhu et al 2003;Blum et al 2004), and information retrieval (Yang et al 2006).

relation extraction is to detect and classify various predefined semantic relations between two entities from text and can be very useful in many NLP applications such as question answering, e.g.


The dynamic methods reduce the miss rates on 5 topics.

this method needs a training corpus to get the extending threshold deciding whether a story should be used to extend another story in a pair.


Orthographic variance is a fundamental problem for many natural language processing applications.

they did not deal with a transliterated probability.


It shows that the MaxEnt method achieves best performance.

all characters in a name are first converted into upper case for ENOR before feature extraction.


As a function of model, c PP measures how good the model matches the test data.

let us use English name "Smith" to illustrate the features that we define.


2) N-gram Perplexity Method (PP): Li et al.

c PP can be used to measure how good a test name matches a training set.


Then they apply edit distance based similarity to select the most probable transliteration in the English text.

the measure used is symmetric cross entropy or SCE (Singh, 2006a).


Finally, a kind of best first search strategy is applied to obtain chunk sequences hopefully in best first order.

the aim here is to assign the highest rank for the correct chunk and to push down other chunks.


Note that no Viterbi search involved here and the state sequence is also known.

we are confident that wide coverage and robust shallow parsing systems can be developed using the UCSG architecture for other languages of the world as well.


Major issues of MT from analytic languages into Indo-European ones include three issues: anaphora generation, semantic duplication, and sentence structuring.

ellipsis-based approach is characterized by treating incomplete constituents as if they are of the same simple type but contain ellipsis inside (Yatabe, 2002;Cryssmann, 2003;Beavers and Sag, 2004).


In the experiments, we trained the parsers on training data and tuned the parameters on development data.

here, we use a simple feature representation on short dependency relations.


Given sufficient labeled data, there are several supervised learning methods for training highperformance dependency parsers .

the goal in dependency parsing is to tag dependency links that show the head-modifier relations between words.


for this evaluation, we employed 500 news articles from Reuters in the health domain gathered between December 2006 to May 2007.


Prodi has said he would call a confidence vote if he lost the Communists' support."

notably, a greedy frequency-driven approach leads to very good results in content selection (nenkova et al., 2006).


We will provide examples of CST relations: 1.

their method used the same features for every type of CST, resulting in low recall and precision.


Finding information about people on huge text collections or on-line repositories on the Web is a common activity.

our best configuration (FD+W) obtains an F-score of 0.74 (or a fourth position in the SemEval ranking).


While these results are rather encouraging, they were not optimal.

metrics used to measure the performance of automatic systems against the human output were borrowed from the clustering literature (Hotho et al., 2003) and they are defined as follows: where C is the set of clusters to be evaluated and L is the set of clusters produced by the human.


Their modifiers are quite semantically diverse, as shown in Table 2.

despite these benefits, STC has not received much attention by the community.


First we extract the pure texts of all Web pages, excluding anchor texts which introduce much noise.

we need to calculate the probability of the occurrence of word w e in language e given a document d in language f , i.e.


On one hand, the confidence measure allows us to adjust the original weights of the translations and to select the best translation terms according to all the information.

the second example is the tREC6 query "Acupuncture".


The query terms are translated with the baseline models (Section 4).

the translation candidates are rescored and filtered according to a more reliable weight.


Only the dependency relationships between content words are extracted.

for example, figure 1 takes the description field of TREC topic 651 as an example and shows part of the parsing result of Minipar.


Here, N is the number of words in the correct transcript, I is the number of incorrectly inserted words, D is the number of deletion errors, and S is the number of substitution errors.

for these reasons, it is difficult to apply the method to a large-scale speech-based IR system.


From this point of view, word error rate (WER), which is the most widely used evaluation measure of ASR accuracy, is not an appropriate evaluation measure when we want to use ASR systems for IR because all words are treated identically in WER.

for well-defined IRs such as relational database retrieval (E. Levin et al., 2000), significant words (=keywords) are obvious.


A word weight should be defined based on its influence on IR.

an estimation method without hand-labeled answer sets, namely, the unsupervised estimation of word weights, is also tested.


best determined by acoustic contrast, since accent type is closely linked to pitch height, and the local context and acoustic features serve to identify which accentable words are truly accented.

conditional Random Fields (Lafferty et al., 2001) are a class of graphical models which are undirected and conditionally trained.


For the four-way contrast between pitch accent types, we see small to modest gains across all feature sets, with the prosodic case improving significantly (p < 0.025).

we jointly predict pitch accent, phrase accent, and boundary tone and, the prediction of each label depends on the features, the other labels predicted for the same syllable, and the sequential label of the same class.


We define four types of named entities: people names (nr), organization names (nt), location names (ns), and numerical expressions (nc) such as calendar, time, and money.

if we know an UNK is a named entity, we can translate this UNK more accurately than using the subword-based approach.


Because of this, their system's coverage and W A were relatively poor than ours 8 .

we can obtain r Chinese transliteration hypotheses and classify them into positive and negative samples according to y i .


Although the states are conceptually clear, it is not necessarily the case that translators can judge the state of a given translation consistently, because judging a sentence as being "natural" or "confusing" is not a binary process but a graded one, and the distinction between different states is often not immedi-ately clear.

in this paper, we examined the factors that trigger modifications when translators are revising draft translations, and identified computationally tractable features relevant to the modification.


Since the new alignment algorithm must enumerate all of the possible alignments, the process is very time consuming.

note that a word can be separated into two substrings each time.


However for remedy, many of the current word alignment methods combine the results of both alignment directions, via intersection or grow-diag-final heuristic, to improve the alignment reliability (Koehn et al., 2003;Liang et al., 2006;Ayan et al., 2006;DeNero et al., 2007).

the second method uses the impurity method, which was motivated by the method of decision tree.


We can not afford losing so much precious time.

for each classifier, we used tenfold crossvalidation and exhaustive search on adjustable parameters in model selection.


These features capture the important aspects of the negotiation process -negotiation-related concepts and indicators of the strategies employed.

electronic means make the contacts less formal, allowing people to communicate more freely.


This kind of clustering is not ideal in a corpus containing a huge variation in event streams, like newswire.

we take a two-step approach to the problem; first, we cluster report sentences based on similarity and second, we extract template(s) corresponding to each cluster by aligning the instances in the cluster.


We also thank the three anonymous reviewers for helpful suggestions.

this method did not look at arbitrary syntactic patterns.


First, we integrate two online databases to extend the coverage of our bilingual dictionaries.

for instance, the Korean NE "메이저리그" and its translation "Major League" are first composed as a query "+ᄆ ᅦ ᄋ ᅵ ᄌ ᅥ ᄅ ᅵ ᄀ ᅳ + Major League", which is then sent to Google.


This results in thousands of possible combinations of Chinese characters, making it very difficult to choose the most widely used one one.

yaser Al-Onaizan (Al-Onaizan and Knight, 2002) transliterated an NE in Arabic into several candidates in English and ranked the candidates by comparing their counts in several English corpora.


Each NE candidate is first sent to the Korean Wikipedia, and the title of the matched article's Chinese version is treated as the NE's translation in Chinese.

• Chinese monolingual: using the Chinese versions of the topics given by NTCIR.


Obviously, the above methods cannot cover all possible translations of NEs.

most non-CJK NEs can be translated correctly by using the K-E translation patterns.


Naver people search does not contain an article either because it is not a person name.

we split the translation process into two stages: the first translates the NE into its English equivalent, and the second translates the English equivalent into Chinese.


Since f ("漂亮" (beautiful)) = "外观" (appearance) and f ("时尚" (fashionable)) = "外 观" (appearance), "外观" (appearance) is an implicit feature in (b).

with the rapid expansion of network application, more and more customer reviews are available online, which are beneficial for product merchants to track the viewpoint of old customers and to assist potential customers to purchase products.


However, it's time-consuming to read all reviews in person.

(b) (外观)漂亮而且(外观)时尚。It's (appearance) beautiful and (appearance) fashionable.


Empirical results on three kinds of product reviews indicate the effectiveness of our method.

with the revised mutual information, the implicit features can be deduced from opinion words.


We can easily obtain the following relation between two scores: Since, according to this relation, score p (S) > score n (S) is equivalent to score p (S) > 0, we use only score p (S) for the rest of this paper.

we can also see that the advantage of sentence-wise becomes smaller as the amount of training data increases, and that the hybrid 3gram model almost always achieved the best accuracy among the three models.


Usually, such models are trained with each occurrence of words as one instance (word-wise learning).

we expect that the score works as a confidence measure.


Consider the following example: I have not had any distortion problems with this phone and am more pleased with this phone than any I've used before.

it is still an open problem how we can effectively use sentiment words to improve performance of sentiment classification of sentences or documents.


As can be seen in Figure 1, the classifier managed to map the reviews onto the coordinate system.

we also found that the accuracy of the method depends a lot on the seed word chosen.


Experiments performed on Miller and Charles similarity data (1991), reported in Jarmasz and Szpakowicz (2004), have shown that pairs of words with a semantic similarity value of 16 have high similarity, while those with a score of 12 to 14 have intermediate similarity.

the effectiveness of this emotion lexicon was demonstrated in the emotion classification tasks.


The effectiveness of this emotion lexicon was demonstrated in the emotion classification tasks.

we select the score of 12 as cutoff, and include in the lexicon all words that have similarity scores of 12 or higher with respect to the words in the primary set.


The reason is that QUEEN is, by definition, a very restrictive measure -a 'good' translation must be similar to all human references according to all metrics.

as to portability across test beds (i.e., across language pairs and years), the reader must focus on the cells for which the meta-evaluation criterion guiding the metric set optimization matches the criterion used in the evaluation, i.e., the top-left and bottom-right 16-cell quadrangles.


The fact that most systems are statistical also explains why, in general, lexical metrics exhibit a higher quality.

human assessments of adequacy and fluency are available for a subset of sentences, each evaluated by two different human judges.


Figure 2 shows some examples of paraphrased dependency relations and paraphrases.

even though a single phrase in a source language sentence maps onto multiple phrases in a foreign language sentence, the phrases might not be paraphrases.


This work is also affiliated with the Microsoft-CUHK Joint Laboratory for Human-centric Computing and Interface Technologies.

one problem concerning relational data is, how to extract useful relations for Chinese NER.


In the spirit of the work done by (Shinyama and Sekine, 2003;Bunescu and Mooney, 2007), we are trying to collect clusters of paraphrases for given relation mentions.

instead of designing the heuristic explicitly, we use a validation set to observe the statistical correlations of each of the three possible heuristics we discussed above.


The filtering of generic patterns (green) does not show 4 Note that Basilisk and Espresso use context patterns only for the sake of collecting instances, and are not interested in the patterns per se.

in previous work, these are surface text patterns, e.g., X such as Y, for extracting words in an is-a relation, with some heuristics for finding the pattern boundaries in text.


The result can be interpreted as a hint that signals for the control of erythropoietin production may be mediated by beta 2-adrenergic receptors rather than by beta 1-adrenergic receptors.

the CRF classifier had roughly 5% advantage on per-abstract accuracy over SVM.


Linguistic annotation guidelines often concentrate on specifying the linguistic data categories to be annotated.

this section demonstrates how OWL DL, a strongly typed representation language, can serve to transparently formalise corpora with multi-level annotation.


Finally, the closest neighbour to our proposal is the ATLAS project (Laprun et al., 2002), which combines annotations with a descriptive meta-model.

we now illustrate these decisions concretely by designing a model for a corpus with syntactic and frame-semantic annotation, more concretely the SALSA/TIGER corpus.


However, the average of R(c a * c d ) in "indirect" cases is not so high for both Chinese and Japanese, as a large amount of pairs are classified into case (A).

as indicated in the fifth row in Table 3, however, many structures consisting of only 2 classifiers were also constructed.


We conducted experiments to build classifier taxonomies for three languages: Chinese, Japanese and Thai.

the average of R(c a * c d ) in "indirect" cases is not so high for both Chinese and Japanese, as a large amount of pairs are classified into case (A).


15 million words) and one for which we had relatively little parallel data (Czech-English news-commentary corpus with approx.

performance is improved by adding component-sequence and learnedmorphology models along with context similarity from monolingual text and optional combination with traditional bilingual-textbased translation discovery.


For example, 1990 is relevant to the query Germany unified because "East and West Germany were unified" according to the top snippet.

the text snippets provide answers to definition answers without actually employing any specialized module for seeking specific information such as the genus of the question concept.


A:000217262,L3 A mother panda often gives birth to two cubs, but when there are two cubs, one is discarded, and young mothers sometimes crush their babies to death.

coverage means the rate of questions that can be answered by the top-N answer candidates.


The system was built as an extension to our factoid QA system, SAIQA (Isozaki, 2004;Isozaki, 2005), and works as follows: 1.

a:000406060,L6 because of the recent development in the midland, they are becoming extinct.


Finally we are interested modifying our cluster-based expansion for the purpose of automatically identifying authority sources for different types of questions.

although expansion methods generate additional relevant documents that simpler methods cannot obtain, an important metric to consider is the density of these new relevant documents.


Rather than manually defining a complete answering strategy -the type of question, the queries to be run, the answer extraction, and the answer merging methods -for each type of question, SQA learns different strategies for different types of similar questions SQA takes advantage of similarity in training data (questions and answers from past TREC evaluations), and performs question clustering.

this paper presents experiments with several feature selection methods used individually and in combination.


At a first glance, document and passage retrieval is reasonable when considering the fact that its performance is often above 80% for this stage in the question answering process.

when evaluated on TREC datasets, the affinity replacement method obtained significant improvements in precision, but did not outperform other methods in terms of recall.


In this experiment, average precision on training data proves to be the best predictor of additional relevant documents: ∼71% of the test questions benefit from queries based on average precision feature selection.

recently (riezler et al., 2007) used statistical machine translation for query expansion and took a step towards bridging the lexical gap between questions and answers.


The goal is to add query content that increases retrieval performance on training questions.

there this query enhancement process is static and does not use the training data and the question answering context differently for individual questions.


Moreover, it is significantly less than the accuracy (0.69) achieved by using the RDC corpus.

in addition, the number of parameters that must be estimated in PLSi grows linearly with the number of training documents.


(Culotta and Sorensen, 2004) extended this work to estimate kernel functions between augmented depen-dency trees, while (Kambhatla, 2004) combined lexical features, syntactic features, and semantic features in a maximum entropy model.

after the latent topic features extracted from returned snippets using the Web as the corpus, an SVM classifier is trained as the relation recognition classifier for use in the later experiments.


The feature captures the interaction between two entities at the semantic level rather than at the word level.

moreover, to address the problem of insufficiently annotated corpora, we propose an algorithm for compiling a training corpus from the Web.


Interestingly, ML falls below the baseline case when more than three languages were used in Test2, a situation that has rarely been considered in previous studies.

this was because PPM would be theoretically equivalent to ML with infinite learning of language transition probabilities, since languages were uniformly distributed in test1.


The user's key entry sequence is input to our client software.

when producing a text in a language other than English, a user has to use text entry software corresponding to the other language which will transform the user's key stroke sequences into text of the desired language.


A text fragment between two delimiters is called a token in TypeAny.

reports on ways to detect a change in the language used are more abundant.


In a usual HMM process, a system finds the language sequence (i.e., state sequence) l m 1 that maximizes Equation (1) by typically using a Viterbi algorithm.

because large corpora are not always available, especially for minor languages, P (t i |l i ) is estimated using key entry sequence probabilities based on n-grams (with maximum n being n max ) as follows: and |t i | is the length of t i with respect to the key entry sequence.


In this case, the user can manually correct the locale by pressing the TAB key once.

this paper addresses the question of how to decrease the need for the third type of action.


flags where both error detection and suggested correction are incorrect.

english is today the de facto lingua franca for commerce around the globe.


We present a modular system for detection and correction of errors made by nonnative (English as a Second Language = ESL) writers.

the distribution of determiners is similar in the PTB (as reported in Minnen et al.


Hence several automatic corpus based approaches for acquiring lexical knowledge have been proposed in the literature.

we recomputed the score for each pattern in the above manner and obtain a ranked list of patterns for each of the classes for English and Hindi.


The entries in WordNet have been classified according to the syntactic category such as: nouns, verbs, adjectives and adverbs, etc.

incorporating more sophisticated methods remains an area of future work.


Moreover, the structure details available in this stage are useful in improving the coherency and readability among the sentences present in the summary.

we have introduced some new feature identification techniques to explore paragraph alignments.


Using the manually annotated subset of the corpus (200 judgments) we have performed a number of preliminary experiments to determine which method would be appropriate for role identification.

we frame text segmentation as a rule learning problem.


To assess the quality of the TimeML projections, we put aside and manually annotated a development set of 101 and a test set of 236 bisentences.

this non-content (NC) filter is defined in terms of POS tags and affects conjunctions, prepositions and punctuation.


960K bisentences) was used for training (section 5).

given that the projected annotations are to provide enough data for training a target language labeller (section 5), manual annotation is not an option.


To estimate precision, 100 relation instances were randomly sampled from each of four sections of the ranks of the acquired instances for each of the two relations (1-500, 501-1500, 1501-3500 and 3500-7500), and the correctness of each sampled instance was judged by two graduate students (i.e.

additionally, the number of seed instances affects the precision of both higher-ranked and lower-ranked instances.


When occurring with the verb suru (do-PRES), verbal nouns function as a verb as in (1a).

finally, but even more importantly, when accompanied by a large variety of suffixes, verbal nouns constitute compound nouns highly productively as in (1c).


For criterion (b), as shown in Table 1, the relation instances judged correct include both the Xga VP 1 ::X-ga VP 2 type (i.e.

(b) there are specific patterns that are highly reliable but they are much less frequent than generic patterns and each makes only a small contribution to recall.


within the punctuation after translation.

unfortunately, no statistically significant improvement on the BLEu score was reported in (Och et al., 2003).


Thus, the baseline and baseline+syn models are not able to produce the correct verb form for "visit".

these algorithms attempt to reconcile the wordorder differences between the source and target language sentences by reordering the source language data prior to the SMt training and decoding cycles.


The prior probability P (D) is usually assumed to be uniform and a language model P (Q|D) is estimated for every document.

the major goal of personalized search is to accurately model a user's information need and store it in the user profile and then re-rank the results to suit to the user's interests using the user profile.


The user profile thus learnt was applied in a re-ranking phase to rescore the search results retrieved using general information retrieval models.

the user profile as a translation model in our approach will consist of triples of a document word, a query word and the probability of the document word generating the query word.


A look at the syntactic variants automatically generated by a system, which we proposed, showed that the system could generate syntactic variants for only a half portion of the input, producing many erroneous ones (Section 4.1).

features extracted from the snippets outperformed newspaper corpus; however, the small numbers of features for phrases shown in Table 7 and the lack of sophisticated weighting function suggest that the problem might persist.


(8) s. "yoi:shigoto:o:suru" (doa good job) t 1 .

it caused errors in some cases; for example, since N 1 was the semantic head in (7), dropping it was incorrect.


While Joachims (1998) and Rogati and Yang (2002) reported no improvement in SVM performance after applying a feature selection step, Gabrilovich and Markovitch (2004) showed that for collection with numerous redundant features, aggressive feature selection allowed SVMs to actually improve their performance.

named Entity Page: refers to a specific object or set of objects in the world, which is/are commonly referred to using a certain proper noun phrase.


the result indicates that there is a considerable correlation (r = 0.760) between category importance and performance, which means it is possible to predict the final performance of any context categories by calculating their category importance values in the limited size of selected context set.


In the following, n and m represent the number of unique words and unique contexts, respectively, and N (w, c) denotes the number of cooccurrence of word w and context c. Document frequency (DF), commonly used for weighting in information retrieval, is the number of documents a term co-occur with.

in this section, context selection methods proposed for text categorization or information retrieval are introduced.


Finally we extend the context importance to cover context categories (RASP2 grammatical relations), and show that the above methods are also effective in selecting categories.

we formalize distributional similarity as a classification problem as described below.


We refer to this measure as DSlesk as defined: where g 1 is the gloss of word sense s1, g 2 is the gloss of s2, again s1 is the target word sense ws i in equation 1 for which we are obtaining the predominance ranking score and s2 is whichever sense of the neighbour (n j ) in equation 1 which maximises this semantic similarity score, as McCarthy et al.

we believe that our gloss-based similarity DSlesk might be very suitable for this task and we plan to investigate the possibility.


In the recent times many efforts have been made to develop various utilities for Sanskrit.

sanskrit is a highly inflected language with three grammatical genders (masculine, feminine, neuter) and three numbers (singular, plural, dual).


ﻡ " ( am) is a suffix denoting first s i n g l e p e r s o n " ﻥ ﺍ ﻮ ﺧ " (xän) is the present tense root o f t h e v e r b a n d " ﯽ ﻣ " ( mi) is a prefix that expresses continuity.

for example, we put the morpheme " ﺮ ﺴ ﭘ "(pesar: boy) in cluster " ﻢ ﺳ ﺍ " (esm: noun) a n d " ﺭ ﺍ ﺪ ﻧ ﺎ ﺟ " ( jändär: alive).


Consequently, for each stored word, we find its stem.

we store these rules in rules repository.


We evaluated the proposed algorithm with a limited corpus of Hamshahri newspaper.

affix stripping approaches try to removing affixes until reaching to any stem in the word.


in the segmentation task, the sentence x are segmented bŷ where F s (•) is the set of segment features, and w s is the parameter for segmentation.


Its computation formula is: θ = f P / ( f P + f N ), where f P is the frequency of the positive examples, and f N is the frequency of the negative examples.

through rule reliability computation (see the following section), we can extract all high-reliability basic rules as the final result, and all other basic rules with higher frequency for further rule refinement.


Table 2 shows some examples from the corpus illustrating some typical types of ESL writing errors involving: (1) Verb-Noun Collocations (VNC) and (4) Adjective-Noun Collocations (ANC); (2) incorrect use of the transitive verb "attend"; (3) determiner (article) usage problems; and (5) more complex lexical and style problems.

a smaller t 3 can reduce recall, but can increase GP.


Note that a randomguessing baseline was about 5% precision, 7% recall, but more than 80% false flag rate.

eSL-WePS first segments the original eSL sentence by using punctuation characters like commas and semicolons, then generates a query from only the part which contains the given check point.


The earlier the correct recommendation is, the larger the effect is.

the following takes the three elements from a popular site as an example.


In this paper, only one intention is assigned to the utterances.

two schemes, revised tf and tf-idf, are employed to classify the utterances in dialogues.


As discussed in 2.2, the most influential feature in the C/NC method is the term frequency.

for this reason, some noises are extracted together with these candidates.


To mitigate the problem, we used Wordnet to handle two different words with the same semantic.

many other relevant information can be missed out as a result.


Other Kanji characters with the same pronunciation as " " include " ".

the first task is transliteration in the strict sense, which creates new words in a target language (Haizhou et al., 2004;Wan and Verspoor, 1998;Xu et al., 2006).


In Figure 2, (d) is such a representation for the two basic alignments.

the output of this part therefore contains syntactic information for structure.


Target language model probability (weight = 0.5) According to a previous study, the minimum error rate training (MERT) (Och, 2003), which is the optimization of feature weights by maximizing the BLEU score on the development set, can improve the performance of a system.

parallel corpus is one of the most important components in statistical machine translation (SMT), and there are two main factors contributing to its performance.


There are few studies on data selection for translation model training.

the range of improvement is not stable because the MERT algorithm uses random numbers while searching for the optimum weights.


This gives a slightly better performance, but again it gives almost identical results for the use of interpolated LMs vs. two LMs as separate feature functions (27.63 vs. 27.64).

training data for LMs often comes from diverse sources, some of them are quite different from the target domain of the MT application.


Finally, hyperlinks are embedded in the hypertext.

forward(i) and backward(i) indicate that the search is carried out relatively to the source (before or after) and indexed by the integer i.


In our approach we specify the source and the target using conditions.

the other operators are: isParent, isChild, isSibling, isDescendant, hasIntitle, isIntitle.


Synset assignment with SC=3 Figure 2 simulates that an English equivalent of a lexical entry L 0 and its synonym L 1 are included in a synset S 1 .

after the revision at KUI, the initial stage of asian WordNet will be referable through the assigned synset ID.


Our approach is conducted to assign a synset to a lexical entry by considering its English equivalent and lexical synonyms.

the extensive development of WordNet in other languages is important, not only to help in implementing NLP applications in each language, but also in inter-linking WordNets of different languages to develop multi-lingual applications to overcome the language barrier.


All words in the lexicon are defined by using 16,900 words in the same lexicon.

a goal of this study is to try to build a Japanese defining vocabulary on the basis of distribution of words used in word definition in an existing Japanese dictionary.


The accuracy of the WOrder parameter drops off geometrically as the number of instances approaches zero, as shown in Table 5.

these values are usually not atomic, and can be decomposed into their permuted elements, which themselves are types.


As discussed, a typology consists of a parameter and a list of possible types, essentially the values this parameter may hold.

summing across such rules might alleviate some of this problem.


Now we will consider different cases for analyzing the time complexity of our MakeRefExpr() algorithm.

later it is extended to take care of different refinements (like relational, boolean description etc) that could not be handled by Incremental algorithm.


To precisely detect reliable parses, we make use of several linguistic features inspired by the notion of controlled language (Mitamura et al., 1991).

we call the two sets of obtained sentences "BIO pool" and "CHEM pool".


Pure syntactic approaches cannot determine boundaries of conjunctive phrases properly.

context-dependent rule patterns are generated and generalized by the following procedure.


The relations among the words in a chunk are not marked for now and hence allow us to ignore local details while building the sentence level dependency tree.

modern dependency grammar is attributed to Tesnière (1959).


There are six basic karakas, namely; adhikarana 'location', apaadaan 'source', sampradaan 'recipient', karana 'instrument', karma 'theme', karta 'agent'.

in our dependency tree each node is a chunk and the edge represents the relations between the connected nodes labeled with the karaka or other relations.


We based this equation on Robertson's equation .

the U.S.A. has held the Text REtrieval Conferences (TREC) (TREC-10 committee, 2001), and Japan has hosted the Question-Answering Challenges (QAC) (National Institute of Informatics, 2002) at NTCIR (NII Test Collection for IR Systems ) 3.


Second, in this method just individual concept is used to determine their saliency, not their combinations.

the SVD-Scores are in favor of using the boosting methods for classification of sentences with different distance measure for each classifier.


The properties of these collections are presented in table 1 To find out which term weighting and distance measure causes the highest increase in the SVD-Scores, various combinations of these approaches has been used in the summarization approach.

some words in Persian are compound words .this did not cause any problem for the developed system; because the most meaningful part of such words is usually less common than others and thus have an Inverse Document Frequency (IDF) that is higher than that of more common less meaningful parts.


The sectional evaluation and the inspection of example output show that this system works well.

the examinee categorized them based on how proper the output summary is to the input news article: 1) quite proper 2) slightly proper 3) not very proper 4) not proper of judgment, 48 outputs out of 77 are evaluated either 1) quite proper or 2) slightly proper.


In the future work, we will investigate if the more sophisticated translation model or that specialized for CLQA task can improve the performance further.

for CLQA1 test collection, we only investigated the result by using R+U judgment.


It was concieved for machine translation tasks, which explains some of its features.

we will keep the term 'preposition' hereafter for all these marks.


Our work focuses on unsupervised and semisupervised methods that target all words and parts of speech (POS) in context.

an extension of the Banerjee and Pedersen (2002) method which makes use of the sense-annotated definitions is to include the words in the definition of each sense-annotated word rather than traversing the ontology relative to each word sense candidate s i,j for the target word w i , we represent each word sense via the original definition plus all definitions of word senses contained in it (weighting each to give the words in the original definition greater import than those from definitions of those word senses).


Our approach makes the best use of an ordinary dictionary and a Web corpus to extract broadcoverage and precise synonym and hypernymhyponym expressions.

here, polysemic words should be treated carefully 3 .


Furthermore, if one SYN node has a hyper synonymous group in the synonymy database, the SYN node with the hyper SYNID is also added.

our method extracts not only hypernym-hyponym relations, but also basic synonym relations, predicate synonyms, adverbial synonyms and synonym relations between a word and a phrase.


To compare our different models we created a test set of 75 categories.

we ranked candidate hyponyms on 75 categories of named entities and attained 53% mean average precision.


This fact inspired us to design and employ the following method.

furthermore, speech recognition has enabled the automation of certain applications that are not automatable using push-button interactive voice response (IVR) systems.


Since we cannot use such a big dictionary in these task, our first results had quite high WERs and IWERs.

we used ∼70% of data for training, ∼15% for development, and the remainding ∼15% for testing.


Section 3 gives a comparative study of phonotactics of the three languages i.e.

spectral subtraction is a noise suppression technique used to reduce the effects of added noise in speech.


In doing so, a good accuracy obtained on the classification task implies that the extracted features capture those aspects of the language that a trigram model may not.

the coherence score for an article is normalized by the total number of content-word pairs found in the article.


In this work, we have used a classification-task based formalism for evaluating various syntactic, semantic and empirical features with the objective of improving conventional language models.


The binary classifiers designed by using LRM-F and SVM-F were trained to maximize the F 1 -score for each category.

with this approach, we assume the independence of categories and design a binary classifier for each category that determines whether or not to assign a category label to data samples.


The IPC taxonomy has four hierarchical layers: Section, Class, Subclass, and Group.

we can expect to improve multi-label classification performance by using binary classifiers trained to maximize the F 1 -score.


A rule-based system consists of a set of rules.

we assume that the rules are given to us and study the problem of arranging them into an optimal decision list, where optimality is determined over a training data set.


In our task, we chose the bottom k related sentences with the lowest certainty scores.

fortunately, active learning systems design strategies to select the most informative training examples.


We finally applied the model to new biomedical articles and examined its performance on one of its subsets.

as every processing unit in the data set is at the sentence level and we make decisions at the sentence level to train better sequential labeling models, we define heuristic scores at the sentence level.


Then the likelihood of labels y = [y 1 , .

given the high degree of human interaction involved, their method will not be scalable to a large number of medical conditions.


In addition, recall that headings, bullet list and ordered list allowed the repetitions of symbols "=", "*" and "#".

this suggests that we could extract different types of hyponymy relations from each of these methods.


(hyponym is one of hypernym) Note that hyponym and hypernym match only with NPs.

note that the non-variable part of the patterns is removed from the matched hypernym candidates.


In other words, as there is no horizontal connection at the same level, it is not possible to create triangle circulation paths in a single stroke.

we selected 98,083 words after removing noise words, functional words, and 1,321 isolated words to extract word pairs by combining every headword with every other headword included within an entry text.


In order to overcome such difficulties in building appropriate lexical graphs for corpus data, we propose an original way of appropriately subdividing core clusters by taking into account graph coefficients, especially the curvature of a hub word.

as there is no horizontal connection at the same level, it is not possible to create triangle circulation paths in a single stroke.


In addition, the adoption of techniques to deal with unknown words  and techniques to combine with rules may also improve the performance of our algorithm.

it substitutes the probability P r the tag of word w i is determined by the tags of the J words right before w i and L words right before w i .


In this work, we introduce a novel approach for automatic correction of spelling mistakes by deploying finite state automata to propose candidates corrections within a specified edit distance from the misspelled word.

we can make use of the finite state automaton representation of the dictionary to make this step more efficient.


Com-pared with (a), it is obvious that the improvement between TSVM using heuristics with TSVM for ARG0 and ARGM-TMP is larger than the overall improvement.

both of them are used for training TSVM model.


The evaluation phase consists in checking the performance of each model for predicting thematic boundaries.

each utterance has been considered as a block of text.


The EM algorithm for AMDD is based on iteratively maximizing the log-likelihood function: .

lCSeg SVMs P k error rate 21% 32 % 22% Table 1: Comparative performance results.


For example, news stories about "A team at Peking University in Beijing studied tissue taken from 2 people killed by H5N1 in China" or "A meeting on foot and mouth disease (FMD) was held in Brussels on 17 th October, 2007".

the remainder of this paper is organized as follows.


These components are depicted in Figure 1.

there are two locations named Camden: One in Australia and one in London, UK.


TTS systems have been developed using the Festival framework for different languages, including English, Japanese, Welsh, Turkish, Hindi, and Telugu (Black and Lenzo, 2003).

empirical estimation of least number of input classes needed for training a neural net for Sinhala character recognition suggested about 400 classes (Weerasinghe et al., 2006).


be an image feature space, and Based on the image data set V, we can estimate an image instance-to-feature co-occurrence matrix A |V|×|F | ∈ R |V|×|F | , where each element A ij (1 ≤ i ≤ |V| and 1 ≤ j ≤ |F|) in the matrix A is the frequency of the feature f j appearing in the instance be a text feature space.

labeled data are often scarce and expensive to obtain.


For the WSsim/SemCor dataset, the correlation between original and WSsim annotation was ρ = 0.234, ρ = 0.448, and ρ = 0.390 for the three annotators, each highly significant with p < 2.2e-16.

5 Unlike previous word sense annotation projects, we asked annotators to provide judgments on the applicability of every WordNet sense of the target lemma with the instruction: 6 2 Throughout this paper, a target word is assumed to be a word in a given PoS.


It uses paraphrases for words in context as a way of annotating meaning.

the percentage of markables that received multiple sense labels in existing corpora is small, and it varies massively between corpora: In the SemCor corpus (Landes et al., 1998), only 0.3% of all markables received multiple sense labels.


In contrast, the macro average does not bias the scores, thus the roles having a small number of instances affect the average more than the micro average.

we thus propose another approach that incorporates group information as feature functions.


Automatic dependency relations were produced by the MALT parser.

the Argument Mapping Predictor uses the following features: (23) Predicate.


We used Score(x i,t ) 7 to replace Score(x i,t ) in our conversion algorithm and then ran the updated algorithm on CDT.

score Interpolation Unlabeled dependency f-scores used in section 2.1 measure the quality of converted trees from the perspective of the source grammar only.


It is important to acquire additional labeled data for the target grammar parsing through exploitation of existing source treebanks since there is often a shortage of labeled data.

for acquisition of better conversion rules, Xia et al.


All the works in Table 8 used CTB articles 1-270 as labeled data.


Thus we managed to collect a named entity translation dictionary to enhance the original one.

the basic idea to support this work is to make use of the semantic connection between different languages.


Above all, a translated word pair list, L, is extracted from the translated treebank.

this extra effort did not receive an observable performance improvement in return.


We hypothesized earlier that lexicalization is unlikely to give us much improvement in performance, because topological fields work on a domain that is higher than that of lexical dependencies such as subcategorization frames.

all productions in the corpus have also been binarized.


Our model views each pair of sentences as having been generated as follows: First an alignment tree is drawn.

we want our model to find tree alignments such that both aligned node pairs and unaligned nodes have high Giza-score.


Given a sample we can obtain the seen vocabulary and the seen number of hapax legomena.

we thus need methods to extrapolate empirical measurements of these quantities to arbitrary sample sizes.


A collection of about 3 million words from varied articles in the Hindi language also from the Central Institute of Indian Languages.


Correct stress placement is important in textto-speech systems because it affects the accuracy of human word recognition (Tagliapietra and Tabossi, 2005;Arciuli and Cupples, 2006).

the ranking approach facilitates inclusion of arbitrary features over both the input sequence and output stress pattern.


Predicting the full stress pattern is therefore inherently more difficult than predicting the location of primary stress only.

unlike typical sequence predictors, we do not have to search for the highest-scoring output according to our model.


We study the use of phonological features and affinity statistics for transliteration alignment at phoneme and grapheme levels.

we arrive at a set of 560,768 English-Chinese (EC) pairs that follow the Chinese phonetic rules, and a set of 83,403 English-Japanese Kanji (EJ) pairs, which follow the Japanese phonetic rules, and the rest 29,219 pairs (REST) being labeled as incorrect transliterations.


Each swarm combines results from six rule sets with varying amounts of pruning (no pruning and pruning with cut-off = 1..5).

the main challenge for the training algorithm is that it must produce rules that accurately lemmatize OOV words.


For Bengali precision was 39.3 percent better than without stemming, though no absolute numbers were reported for precision.

we explain how the lemmatization rules are created and how the lemmatizer works.


The last rule's pattern matches any word and so the lemmatizer cannot fail to produce output.

to the DAG, the tree implements negation: if the N th sibling of a row of children fires, it not only means that the pattern of the N th rule matches the word, it also means that the patterns of the N-1 preceding siblings do not match the word.


In all cases, we prune the lattices/hypergraphs to a density of 30 using forward-backward pruning (Sixtus and Ortmanns, 1999).

because the right-hand side of r e has n nonterminals, the arity of e is |e| = n. Let T (e) = {v 1 , ..., v n } denote the tail nodes of e. We now assume that each tail node v i ∈ T (e) is associated with the upper envelope over all candidate translations that are induced by derivations of the corresponding nonterminal symbol X i .


When the arity of the edge is 2, a rule has the general form aX 1 bX 2 c, where X 1 and X 2 are sequences from tail nodes.

translation lattices contain a significantly higher number of translation alternatives relative to Nbest lists.


Therefore, by doing so, the tree sequence rules can be extracted from a forest in the following two steps: 1) Convert the complete parse forest into a non-complete forest in order to cover those tree sequences that cannot be covered by a single tree node.

with this platform, we can easily implement our method and many previous syntax-based methods by simple parameter setting.


Fundamentally, syntax-based SMT views translation as a structural transformation process.

in Table 2, partially-lexicalized rules extracted from training corpus are the major part (more than 70%).


Examples of such overviews include actor biographies from IMDB and disease synopses from Wikipedia.

for instance, some approaches coarsely discriminate between biographical and non-biographical information (Zhou et al., 2004;Biadsy et al., 2008), while others go beyond binary distinction by identifying atomic events -e.g., occupation and marital status -that are typically included in a biography (Weischedel et al., 2004;filatova and Prager, 2005;filatova et al., 2006).


Participants directly express their opinions, such as "The iPhone is cool," but, more often, they mention associated aspects.

here again, we use the process described in Section 3.1 to extract polarity-target pairs for each opinion expressed in the post.


Thus, one major source of errors is a false hit of a word in the lexicon.

we need to find opinions and pair them with targets, both to mine the web for general preferences and to classify the stance of a debate post.


Additionally, we see that some conclusions of the OpPMI system are similar to those of the OpPr system, for example, that "Storm" is more closely related to the Blackberry than the iPhone.

1 In this work, we deal only with dual-sided, dual-topic debates about named entities, for example iPhone vs. Blackberry, where topic 1 = iPhone, topic 2 =Blackberry, side 1 = pro-iPhone, and side 2 =pro-Blackberry.


This is expected, because it relies only on opinions explicitly toward the topics.

previous work did not account for concessions in determining whether an opinion supports one side or the other.


For each of the 4 debates in our test set, we use posts with at least 5 sentences for evaluation.

an opinion expressed about "Storm" is usually the opinion one has toward "Blackberry."


We propose a number of characteristics of good sentiment terms from the perspectives of informativeness, prominence, topic-relevance, and semantic aspects using collection statistics, contextual information, semantic associations as well as opinion-related properties of terms.

this paper introduces an approach to the sentiment analysis tasks with an emphasis on how to represent and evaluate the weights of sentiment terms.


We also thank the anynomous reviewers of the previous drafts of this paper for their valuable suggestions in improving the evaluation and presentation.

the set {B, C} has nodes that have both senses, forming an ambiguity set.


Recent studies Kozareva et al., 2008) show that if the size of a corpus, such as the Web, is nearly unlimited, a pattern has a higher chance to explicitly appear in the corpus.

based on different definitions of Count(.


Each term insertion yields a new partial taxonomy T. By the minimum evolution assumption, the optimal next partial taxonomy is one gives the least information change.

the KnowItAll system  extended the work in (Hearst, 1992) and bootstrapped patterns on the Web to discover siblings; it also ranked and selected the patterns by statistical measures.


The basic idea underlying the algorithm is that if the dynamic range of the perceptron is not too large then w t would classify most instances correctly most of the time (for most values of t).

reidsma and Carletta (2008) recently showed by simulation that different types of annotator behavior have different impact on the outcomes of machine learning from the annotated data.


We show in section 3.4 that the widely used Freund and Schapire (1999) voted perceptron algorithm could face a constant hard case bias when confronted with annotation noise in training data, irrespective of the size of the dataset.

e(ζ 2 ) ≥ min{H T (δ), H γ (δ)}, and Corollary 4 The bound in theorem 2 does not converge to zero for large N .


The third conclusion we can draw then is twofold.


If we read these tables column-wise, thereby taking the more linguistically-inspired labels in VerbNet to be the reference labels, we observe that the labels in PropBank are especially concentrated on those labels that linguistically would be considered similar.

2 Furthermore, we perform our analyses on training and development data only.


Semantic roles are organized according to the thematic hierarchy (one proposal among many is Agent > Experiencer> Goal/Source/Location> Patient (Grimshaw, 1990)).

both annotation schemes could be useful in different circumstances and at different frequency bands.


future work will have to assess the effectiveness of individual features and investigate ways to customize RTE systems for the MT evaluation task.


Similar ideas have been applied by Owczarzak et al.

entailment relations are more sensitive to the contribution of individual words (MacCartney and Manning, 2008).


In order to check the robustness of these results, we computed the correlation of individual metric failures between test beds, obtaining 0.67 Pearson for the lowest correlated test bed pair (AE 2004 and CE 2005 ) and 0.88 for the highest correlated pair (AE 2004 and CE 2004 ).

linguistic metrics are represented by grey plots, and black plots represent metrics based on n-gram overlap.


Figure 1 shows the correlation obtained by each automatic evaluation metric at system level (horizontal axis) versus segment level (vertical axis) in our test beds.

the main reason is that the advantages of employing deeper linguistic information have not been clarified yet.


Since it is not linguistically motivated, original phrasebased decoding might produce ungrammatical or even wrong translations.

we add a new feature into the log-linear translation model: P SDB (b|T, τ (.)).


In a projective dependency tree, the yield of every subtree is a contiguous substring of the sentence.

more precisely, if the next node in the projective order is the kth node in the buffer, we perform k SHIFT transitions, to get this node onto the stack, followed by k−1 SWAP transitions, to move the preceding k − 1 nodes back to the buffer.


An improved algorithm computes, for each possible edge k → i, a modified Kirchoff matrix K k→i that requires the presence of that edge.

gE training of the full CRF outperforms EM with 10 constraints and CE with 20 constraints (those displayed in Table 1).


The development and tuning of the above methods constitute the encoding of prior domain knowledge about the desired syntactic structure.

we also report the accuracy of an attach-right baseline 6 .


Seginer (2007) and Bod (2006) propose unsupervised phrase structure parsing methods that give better unlabeled F-scores than DMV with EM, but they do not report directed dependency accuracy.

the CRF may consider the distance between head and child, whereas DMV does not model distance.


We first try to add the transferred edges in random order, then for each orphan node we try all possible parents (both in random order).

if we specify just the two rules for "da" and verb conjugations performance jumps to that of training on 60-70 fully labeled sentences.


The basic data structures are a stack, where the constructed dependency graph is stored, and an input queue, where the unprocessed data are put.

conceptually, this conversion is similar to the conversions from deeper structures to GR reprsentations reported by clark and curran (2007) and .


The next decision is how to sample S rand from L hand .

this manual evaluation is extremely time consuming and is necessary due to the limited coverage of biomedical resources.


This paper proposes a novel framework for a large-scale, accurate acquisition method for monolingual semantic knowledge, especially for semantic relations between nominals such as hyponymy and meronymy.

if this is the case, the effect of adding the translation to the training data can be quite large, and the same level of effect may not be achievable by a reasonable amount of labor for preparing the training data.


Let hyper be a hypernym candidate, hypo be a hyper's hyponym candidate, and (hyper, hypo) be a hyponymyrelation candidate.

in the case of hyponymy-relation acquisition in English and Japanese, (s, t) ∈ D Bi could be (s=(enzyme, hydrolase), t=(Þ (meaning enzyme), AÄ$F Þ (meaning hydrolase))).


In order to reduce irrelevant chunks, when excerpts were extracted, the Provider drops all characters preceding the hyponym phrase in excerpts that contain the first type, and also drops all characters following the hyponym phrase in excerpts that contain the second type.

unlike all of those systems, ASIA does not use any NLP tool (e.g., partsof-speech tagger, parser) or rely on capitalization for extracting candidates (since we wanted ASIA to be as language-independent as possible).


We conjecture that in other application settings the rules extracted from Wikipedia might show even greater marginal contribution, particularly in specialized domains not covered well by Word-Net.

as the results with this filter resemble those for Dice we present results only for the simpler Dice filter.


In addition, the items in each semantic class need to be properly ordered.

multi-membership is more popular than at a first glance, because quite a lot of English common words have also been borrowed as company names, places, or product names.


when we input "gold" as the query, the item "silver" can only be assigned to one semantic class, although the term can simultaneously represents a color and a chemical element.


We assume that the parse trees of s and t are known.

we discuss next how to parameterize the probability p kid that appears in Equations 4, 5, and 6.


The CRF++ version 0.50, a popular CRF library developed by Taku Kudo, 6 is reported to take 4,021 seconds on Xeon 3.0GHz processors to train the model using a richer feature set.

the test set was used only for the final accuracy report.


This is because otherwise the accuracy of the component models would be overestimated by the joint model.

we show the performance on lemmatization when tags are not predicted (Tag Model is none), and when tags are predicted by the tag-set model.


Several authors investigate neural network models that learn not just one latent state, but rather a vector of latent variables, to represent each word in a language model (Bengio et al., 2003;Emami et al., 2003;Morin and Bengio, 2005).

what is the effect of smoothing on sequencelabeling accuracy for rare word types?


In our next experiment, we consider a common scenario where rare terms make up a much larger fraction of the test data.

we take ten random samples of a fixed size from the labeled training set, train a chunking model on each subset, and graph the F1 on the labeled test set, averaged over the ten runs, in Figure 1.


We investigate the use of distributional representations, which model the probability distribution of a word's context, as techniques for finding smoothed representations of word sequences.

the distribution for a corpus x = (x 1 , .


For the small toy example shown in Figure 3, the correct tagging is "PRO AUX V .

we run EM training again for Model 5 (the best model from Figure 5) but this time using 973k word tokens, and further increase our accuracy to 92.3%.


Since we were interested in finding an optimal combination of word-level and characterlevel nodes for training, we focused on tuning r. We fixed N = 10 and k = 5 for all experiments.

this section discusses the structure of f (x, y).


We envision this technique to be general and widely applicable to many other sequence labeling tasks.

we also thank Yang Liu and Haitao Mi for helpful discussions.


For example, b-NN indicates that the character is the begin of a noun.

the compositive error rate of Recall for all word clusters is reduced by 20.66%, such a fact invalidates the effectivity of annotation adaptation.


That is, we repeated the experiment, in which we used one discourse from among 16 discourses as the test data and the others as the learning data, 16 times.

among 1,119 incorrectly inserted linefeeds, the most frequent cause was that linefeeds were in- T  h  a  t  i  s   t  h  e  p  e  r  i  o  d  w  h  i  c  h  I  c  a  l  l   t  h  e  f  i  r  s  t  p  e  r  i  o  d   w  i  t  h  o  u  t  a  p  o  l  o g y Figure 6: Example of incorrect linefeed insertion in "adnominal clause."


This ratio is less than half of that for all the bunsetsu boundaries.

in the clause boundaries of the "adnominal clause" type, linefeeds should rarely be inserted fundamentally.


In this section, we discuss the causes of the incorrect linefeed insertion occurred in our method.

the F-measure and the sentence accuracy of our method were 81.43 and 53.15%, respectively.


Our experimental results using co-training are significantly better than the original supervised results using the small amount of training data, and closer to that using supervised learning with a large amount of data.

our confidence-based approach balances the number of positive and negative samples and significantly reduces the error  rates for the negative samples as well, thus leading to performance improvement.


An obvious way to summarize multiple spoken documents is to adopt the transcribe-andsummarize approach, in which automatic speech recognition (ASR) is first employed to acquire written transcripts.

in this paper, the similarity of utterances is estimated directly from recurring acoustic patterns in untranscribed audio sequences.


For example, we can express BLEU(e; e ) = exp In this expression, BLEU(e; e ) references e only via its n-gram count features c(e , t).

(2008) primarily in that we propose decoding with an alternative to MBR using BLEU, while they propose decoding with MBR using a linear alternative to BLEU.


The space of similarity measures is large and relatively unexplored, and the feature expectations that can be computed from forests extend beyond n-gram counts.

first, word lattices are a subclass of forests that have only one source node for each edge (i.e., a graph, rather than a hyper-graph).


Forest-based consensus decoding leverages information about the correct translation from the entire forest.

they derive a first-order taylor approximation to the logarithm of a slightly modified definition of corpus BLEU 4 , which is linear in n-gram indicator features δ(e , t) of e .


Statistical machine translation is a decision problem where we need decide on the best of target sentence matching a source sentence.

we have presented a framework for including multiple translation models in one decoder.


All models can be combined at the translation level.

we can use the Bisection method for finding the intersection in each bin.


From the results we can see that combination based on co-decoding's outputs performs consistently better than that based on baseline decoders' outputs for all n-best sizes we experimented with.

the work on multi-system hypothesis selection of Hildebrand and Vogel (2008) bears more resemblance to our method in that both make use of n-gram agreement statistics.


We  We use the parallel data available for the NIST 2008 constrained track of Chinese-to-English machine translation task as bilingual training data, which contains 5.1M sentence pairs, 128M Chinese words and 147M English words after pre-processing.

the results for hypothesis selection are only slightly better than the best system in co-decoding.


Lastly, the definition of the n-gram model is different.

we also showed that interpolating variational models with the Viterbi approximation can compensate for poor approximations, and that interpolating them with one another can reduce the Bayes risk and improve BLEU.


To obtain p(y | x) above, we need to marginalize over a nuisance variable, the derivation of y.

we use standard beam-pruning and cube-pruning parameter settings, following Chiang (2007), when generating the hypergraphs.


# $ % & Figure 1: Segmentation ambiguity in phrase-based MT: two different segmentations lead to the same translation string.

in order to score well on the BLEU metric for MT evaluation (Papineni et al., 2001), which gives partial credit, we would also like to favor lower-order ngrams that are likely to appear in the reference, even if this means picking some less-likely highorder n-grams.


Third, all possible MRs for the sentence are constructed compositionally in a recursive, bottom-up fashion following its syntactic parse using composition rules.

a child MR becomes an argument of the macro-predicate if it is complete (i.e.


For example, consider the task of annotating WN with the labeled class renaissance painters containing the class instances Pisanello, Hieronymus Bosch, and Jan van Eyck and associated with the attributes "famous works" and "style."

there is also a pressure for these two occurrences to attach to a single concept.


Regression fit and regression-based classification rank accuracy of the adjective, noun, additive, and multiplicative models for phrase stimuli.

nothing was done to elicit consistency across participants.


Classification accuracies were significantly higher (p < 0.05) for the nouns, calculated with a paired t-test.

meaning associated with the noun should be more evoked.


We quantitatively measure the impact of each of these subproblems on coreference resolution performance as a whole.

we see that focusing attention on all and only the annotated CEs leads to (often substantial) improvements in performance on all metrics over all data sets, especially when measured using the MUC score.


Bengtson and Roth (2008) simply discard twinless CEs, but this solution is likely too lenient -it doles no punishment for mistakes on twinless annotated or extracted CEs and it would be tricked, for example, by a system that extracts only the CEs about which it is most confident.

we would expect a coreference resolution system to depend critically on its Named Entity (NE) extractor.


Just as for the polarity features, we include features for both each tag and its negation.

since the learning curve is steeper when function words were removed, they hypothesize that using only non-function words will outperform using all words once enough training data is available.


Our last baseline implements the active learning procedure as described in Tong and Koller (2002).

given that we now have a labeled set (composed of 100 manually labeled points selected by active learning and 500 unambiguous points) as well as a larger set of points that are yet to be labeled (i.e., the remaining unlabeled points in the training folds and those in the test fold), we aim to train a better classifier by using a weakly supervised learner to learn from both the labeled and unlabeled data.


In contrast, the goal of this paper is to model and classify such speaker attributes from only the latent information found in textual transcripts.

to assess the potential gains from full exploitation of partner-sensitive modeling, we first report the result from an oracle experiment, where we assume we know whether the conversation is homogeneous (same gender) or heterogeneous (different gender).


% of pronoun usage: Macaulay (2005) argues that females tend to use more third-person male/female pronouns (he, she, him, her and his) as compared to males.

on top of the standard Boulis and Ostendorf (2005) model, we also investigated the following features motivated by the sociolinguistic literature on gender differences in discourse (Macaulay, 2005): 1.


Table 2 that, when the number of labeled data is small (n i L < 10% * n L ), graph based SSL, gSum SSL, has a better performance compared to SVM.

here we give brief descriptions of only the major modules of our QA due to space limitations.


If its immediate k neighbors, dark blue colored nodes, have the same label, the algorithm continues to search for the secondary k neighbors, the light blue colored nodes, i.e., the neighbors of the neighbors, to find out if there are any opposite labeled nodes around.

each q/a pair is represented as a feature vector x i ∈ d characterizing the entailment information between them.


Glosses and definitions for the same lexeme in different lexical semantic and encyclopedic resources can actually be considered as near-paraphrases, since they define the same terms and hence have  We use glosses and definitions contained in the following resources to build a parallel corpus: • WordNet (Fellbaum, 1998).


Translation-based retrieval models have been widely used in practice by the IR and QA community.

the correct answer to the first question "Who invented Halloween?"


Our proposed method performs better than the best method in the TAC 2008 competition.

topic and opinion are not independent in reality.


We thus consider two path simplifications as well: • compressed: only the source, target, and root nodes are preserved in the path (so the path above becomes [VBP ↑ S ↓ NP]) • POS class clusters: rather than distinguish, for example, between different tenses of verbs in a path, we consider only the first letter of each NT.

• Given a Levenshtein alignment between altered rules, the most common changes within a given NT phrase are detailed in column five of Table 1.


We begin by reviewing previous work in the automatic labeling of structural semantics and motivating the analysis not only in terms of discovery but also regarding its potential application to automatic speech reconstruction research.

only these utterances (about 72% of the annotated SSR data) can be given a semantic analysis in the following sections.


The language model is also re-trained as described in Section 3.4.

first, we assume P LM (c 1 ) ≈ P LM (W ).


Some new words are clearly good for human sense and definitely convey novel semantic information, but they can be useless for speech recognition.

the proposed LAICA approach tries to focus on those new words which can not be handled well by simple character n-grams.


This procedure can be iterated to give Lex i and LM i until convergence.

it's almost impossible to include all words into a lexicon both due to the technical difficulty and also the fact that new words are created continuously.


Our own lectures consist of eleven lectures of approximately 50 minutes each, recorded in three separate courses, each taught by a different instructor.

one of the main challenges to integrating text transcripts into archives of webcast lectures is the poor performance of ASR systems on lecture transcription.


These results are found to be statistically very significant (p ≤ .01).

6 For tuning and testing, we use the official NIST MT evaluation data for Chinese from 2002 to 2008 (MT02 to MT08), which all have four English references for each input sentence.


In our experiments, we use a re-implementation of the Moses phrase-based decoder .

the difference on MT08 is significant in terms of TER.


Moreover, the inference procedure for each sentence pair is non-trivial, proving NP-complete for learning phrase based models  or a high order polynomial (O(|f | 3 |e| 3 )) 1 for a sub-class of weighted synchronous context free grammars (Wu, 1997).

, r n ), and z i denotes the root node of r i .


In this way each process is sampling according to a slightly 'out-of-date' distribution.

although efficient, the sheer number of somewhat arbitrary heuristics makes this approach overly complicated.


In the example sentence, the whole clause "that he was hit by Jack" forms the object of the verb said, and hence is represented in a scope.

training, tuning, and decoding were performed using the Moses toolkit 3 .


While translating from a language of moderate case marking and morphology (English) to one with relatively richer case marking and morphology (Hindi), we are faced with the problem of extracting information from the source language sentence, transferring the information onto the target side, and translating this information into the appropriate case markers and morphological affixes.

it is imperative to make it possible for the system to learn general rules for morphology and case marking.


The paradigm is characterized by a separation between realization and selection, in which rule-based methods are used to generate a space of possible paraphrases, and statistical methods are used to select the most likely realization from the space.

this kind of news-source-explanation is customary to place at the beginning of a sentence in Chinese.


Human usually compress sentences by dropping the intermediate nodes in the dependency tree.

in order to consider the interdependence of words, we employ the Minimum Classification Error (MCE) learning framework (Juang and Katagiri, 1992), which was proposed for learning the goodness of a sequence.


When using the MCE framework, the misclassification measure is defined as the difference between the score of the reference sentence and that of the best non-reference output and we optimize the parameters by minimizing the measure.

the differences in the scores are not significant.


sentence preprocessing mainly includes POs tagging and dependency parsing for the input sentences, as POs tags and dependency information are necessary for matching the paraphrase pattern and collocation resources in the following stages.


We will define a set of probabilistic context-free rules, which generates bags (i.e.

despite this importance, there has been relatively little published work on semantic tagging of web search queries.


This combination provides a framework that benefits from the advantages of both generative and discriminative models.

we used the optional non-terminals to make the task of defining the grammar easier.


Moreover, based on the assumption that anchor texts in different languages referring to the same web page are possibly translations of each other, (Lu et al., 2004) propose a novel approach to construct a multilingual lexicon by making use of web anchor texts and their linking structure.

the second is that the pronunciation similarity of the two words is above a certain threshold so that one can be considered the transliteration of the other.


That means "[N]", "[P]" and "[S]" will be transformed into "[\d]+", "[\p{P}]+" and "[\s]+" respectively.

of the segmentation, the inner text of every node will look like "…ECECC 5 EC…".


Here, we use a very simple rule: in the English snippet, we regard all characters within (and including) the first and the last English letter in the snippet as the English content; similarly, in the Chinese snippet we regard all characters within (and including) the first and the last Chinese character in the snippet as the Chinese content; b) Word segmentation of the Chinese content.

our approach contains four steps: 1) Preprocessing: parse the web page into a DOM tree and segment the inner text of each node into snippets; 2) Seed mining: identify potential translation pairs (seeds) using an alignment model which takes both translation and transliteration into consideration; 3) Pattern learning: learn generalized patterns with the identified seeds; 4) Pattern based mining: extract all bilingual data in the page using the learnt patterns.


When evaluating this new system, we will include similar measures to those described here to enable the evaluations of the two systems to be compared.

automated evaluation metrics that rate system behaviour based on automatically computable properties have been developed in a number of other fields: widely used measures include BLEU (Papineni et al., 2002) for machine translation and ROUGE (Lin, 2004) for summarisation, for example.


The overall correct-assembly rate was correlated with the overall rate of remembering objects: R 2 = 0.20, p < 0.005.

in this paper, we apply a PARADiSE-style process to the results of a user study of a human-robot dialogue system.


In future studies, we will also gather data on these additional non-verbal behaviours, and we expect to find higher correlations with subjective judgements.

subjects who said that they did remember how to build a snowman or an L shape the second time around were no more likely to do it correctly than those who said that they did not remember.


Some studies look into the impact of training data on user simulations.

since most of these current user simulation techniques use probabilistic models to generate user actions, how to set up the probabilities in the simulations is another important problem to solve.


Therefore, this time the simulation gives a correct student answer based on the probability P (c|3rdLaw, ic).

since it is hard to generate a natural language utterance for each tutor's question, we use the student answers in the human user corpus as the candidate answers for the simulated students.


The accuracy of the learned probabilities becomes more questionable when the collected human corpus is small.

most current simulation models are probabilistic models in which the models simulate user actions based on dialog context features (Schatzmann et al., 2006).


¥ One utterance must contain four words or less.

this group had an average WD score of .199, better than the rest of the group at .268.


The expressiveness of the model also slacks due to their weak ability of generalization.

as discussed above, the problem lies in that the non-contiguous phrases derived from the contiguous tree sequence pairs demand greater reliance on the context.


Additionally, for STSSG we set , and for SncTSSG, we set .

this distortional operation, like phrase-based models, is much more flexible in the order of the target constituents than the traditional syntax-based models which are limited by the syntactic structure.


As with general one-to-one matchings, we can optimize margin-based objectives.

the first type comprises bias features for each block length.


This suggests that the intersection word alignment-based expansion method is more effective than the commonly used direct wordalignment-based hypothesis alignment method in confusion network-based MT system combination.

the aligned words are reordered according to their alignment indices.


The hypothesis is modified to match the reference, where a greedy search is used to select the set of shifts because an optimal sequence of edits (with shifts) is very expensive to find.

to reduce the possibility of breaking a syntactic phrase, we extend to choose one of the two above operations depending on which one has the higher likelihood with the current null-aligned word.


The column 'reversal' shows the impact of deliberately bad order, viz.

in distortion model 2 the word sequences are those sequences available in one of the component translations in the CN.


In conventional IHMM, the transition from state i to j has probability: It is tempting to apply the same formula to the transitions in incremental IHMM.

alignment over multiple sequential patterns has been investigated in different contexts.


The alignment of hypothesis 2 against the backbone cannot be considered an error if we consider only these two translations; nevertheless, when added with the alignment of another hypothesis, it produces the low-quality CN in Figure 1b, which may generate poor translations like "he bought a laptop laptop".

the transition from i to i is in fact composed of the first transition from i to δ(i, k) and the second transition from δ(i, k) to the null state at i.


Note also that E(k) represents a word sequence with inserted empty symbols; the sequence with all inserted symbols removed is known as the compact form of E(k).

all the cell sequences S(i, i ) can be classified, with respect to the length of corresponding n-grams, into a set of parameters where each element (with a particular value of n) has the probability The probability of the transition from i to i is: the transition probability of incremental IHMM is a weighted sum of probabilities of 'ngram jumping', defined as conventional IHMM distortion probabilities.


Word alignment between a backbone (or skeleton) translation and a hypothesis translation is a key problem in this approach.

a length limit L is imposed such that for all state transitions where |i − i| ≤ L, the transition probability is calculated as equation 4, otherwise it is calculated by: for some q between i and i .


The last inequality characterizes the amount of work done in the bottom-up pass.

it is very inefficient on its own, but it leads to the full algorithm.


Consistency also implies that items are popped off the agenda in increasing order of bounded Viterbi scores: β(e) + h(e) We will refer to this monotonicity as the ordering property of A * (Felzenszwalb and McAllester, 2007).

if all inside items are processed before any derivation items, the subsequent number of derivation items and outside items popped by KA * is nearly identical to the number popped by EXH in our experiments (both algorithms have the same ordering bounds on which derivation items are popped).


Example 3 Let p be defined as in example 2 and let X = {X A 1 , X A 2 , X A 3 }.

at each iteration, the algorithm performs a reduction by arbitrarily choosing a pair of adjacent endpoint sets from the agenda and by merging them.


Since X has fan-out ≤ 2, E(X) contains at most 4 endpoints.

lCFRS productions with a relatively large number of nonterminals are usually observed in real data.


where V N and V T are disjoint alphabets of non-terminal and terminal symbols, respectively, S ∈ V N is the start symbol, and I and A are finite sets of initial and auxiliary trees, respectively.

the definition of TT-MCTAG imposes that the head tree of each tuple contains at least one lexical element.


For each sentence we extract a dependency path between each pair of entities.

whereas the supervised training paradigm uses a small labeled corpus of only 17,000 relation instances as training data, our algorithm can use much larger amounts of data: more text, more relations, and more instances.


The kd-tree algorithm (Bentley 1980) aims at speeding up nearest neighbor search.

for each input token sequence, we identify all sequences of tokens that are found in the phrase clusters.


As Figure 2 reveals, the learning curves of SeSAL stop early (on MUC7 after 12,800 tokens, on PENNBIOIE after 27,600 tokens) because at that point the whole corpus has been labeled exhaustively -either manually, or automatically.

we propose a semisupervised AL approach for sequence labeling where only highly uncertain subsequences are presented to human annotators, while all others in the selected sequences are automatically labeled.


Intuitively, the former should be given more importance than its constituents 'world' and 'bank', since the meaning of the original phrase cannot be predicted from the meaning of either constituent.

when performing experiments on each query set with the one-parameter and the multiparameter models, the other two query sets have been used for learning the optimal parameters.


We start the construction of our data set by retrieving the queries, together with the clicked urls, from the Yahoo!

(Kruengkrai et al., 2005) proposed a feature based on alignment of string kernels using suffix trees, and used it in two different classifiers.


Furthermore, it is suggested in (Levering and Cutler, 2006) that the average textual content in a web page is 474 words.

this is due to the characteristics of those languages, which allow the construction of composite words from multiple words, or have a richer morphology.


In Section 4, we implement some of the existing models and compare their performance on our test set.

of these operations, we have, for each query q ∈ Q, a set of triplets (l, f l , c u,l ) where l is a language, f l is the count of clicks for l (which we obtained through the urls in language l), and c u,l is the count of unique urls in language l. The resulting table T 3 associates queries with languages, but also contains a lot of noise.


Web search quality can vary widely across languages, even for the same information need.

this section addresses these steps in turn.


We find that our bilingual rankings have good monolingual ranking properties.

for these queries, relevant information at Chinese side may be relatively poorer, so the English ranking can be more reliable.


English is a right-branching language, and so dependents tend to occur after their heads.

for single word connectives, this might correspond to the POS tag of the word, however for multi-word connectives it will not.


It is possible that different connectives have different syntactic contexts for discourse usage.

in sentence (2b), once occurs with a non-discourse sense, meaning "formerly" and modifying "used".


For instance, while the Brazilian striker Ronaldo is rendered as 朗拿 度 long5-naa4-dou6 in Cantonese, other phonetically similar candidates like 朗娜度 long5-naa4-dou6 or 郎拿刀 long4-naa4-dou1 1 are least likely.

hence naming is more of an art than a science, and automatic transliteration should avoid over-reliance on the training data and thus missing unlikely but good candidates.


From the research point of view, it suggests more should be considered in addition to grapheme mapping for handling Cantonese data.

if there happens to be a skewed distribution of a certain Chinese character, the model might preclude other acceptable transliteration alternatives.


Among which, 7.2% involved rarely used character, and 98.4% were assigned common classifications of their causes by human subjects.

the incorrect words were entered into computers based on students' writings, ignoring those characters that did not actually exist and could not be entered.


Experimental results show that using intuitive Web-based statistics helped us capture only about 75% of these errors.

the Web statistics are good for comparing the attractiveness of incorrect characters for computer assisted item authoring.


Because it is nearly impossible to obtain enough test data for any error rate, we generate pseudo test data in the same way that we generate development data.

we feel that this strategy can model both the intentional and un-intentional human error patterns.


We hypothesize that the performance degradation probably results from the spelling errors of the test data, and the inconsistencies that exist between the training data and the test data.

after completing the two steps described in Section 2.1 and 2.2, we have acquired the new spacing states for the user input generated by the baseline WS model, and the threshold measuring the word spacing quality of the user input.


• Depending on the context, even a common word may have different POS tags.

for unknown words, we perform simple morphological analysis to determine probable tags.


Generally, most POS taggers for Indian langauages use morphological analyzer as a module.

another problem was that a clearly defined POS tagset for assamese was unavailable to us.


Many words that occur in natural language texts are not listed in any catalog or lexicon.

building morphological analyzer of a particular Indian language is a very difficult task.


An auxiliary tree β is monotonic if adjoining β to any partial parse tree is monotonic.

the first and second approaches can prevent the parser from infinitely producing partial parse trees, but the parser has to produce partial parse trees as shown in Figure 1.


Our standard CKY parser and Gibbs sampler were both written in Perl.

this compels heuristic methods of subtree extraction, or maximum likelihood estimators which tend to extract large subtrees that overfit the training data.


We examined sentence-level F 1 scores and found that the use of larger subtrees did correlate with accuracy; however, the low overall accuracy (and the fact that there are so many of these large subtrees available in the grammar) suggests that such rules are overfit.

tSGs extend CFGs (and their probabilistic counterparts, which concern us here) by allowing nonterminals to be rewritten as subtrees of arbitrary size.


The morpheme-based representation above cannot explicitly state the boundaries of bunsetsus.

we cannot directly compare results with ours.


The CCG parser has been extensively evaluated elsewhere (Clark and Curran, 2007), and arguably GRs or predicate-argument structures provide a more suitable test set for the CCG parser than PTB phrase-structure trees.

clark and curran (2007) shows that converting gold-standard ccG derivations into the GRs in DepBank resulted in an Fscore of only 85%; hence the upper bound on the performance of the ccG parser, using this evaluation scheme, was only 85%.


Whether PTB parsers could be competitive on alternative parser evaluations, such as those using GR schemes, for which the CCG parser performs very well, is an open question.

there are a number of sentences which are correct, or almost correct, according to EVALB after the conversion, and we are able to use those for a fair comparison.